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Finding Related Micro-blogs Based on WordNet

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 7240)

Abstract

In the common formulation, the recommendation problem is reduced to the problem of estimating the utilization for the items that have not been seen by a user [1]. Micro-blog recommendation will recommend micro-blogs interest users, mostly those related to the micro-blogs that a user had issued or trending topics. One indispensable step in realizing effective recommendation is to compute short text similarities between micro-blogs. In this paper, we utilize two kinds of approaches, traditional cosine-based approach and WordNet-based semantic approach, to compute similarities between micro-blogs and recommend top related ones to users. We conduct experimental study on the effectiveness of two approaches using a set of evaluation measures. The results show that semantic similarity based approach has relatively higher precision than that of traditional cosine-based method using 548 twitters as dataset.

Keywords

  • Semantic Similarity
  • Semantic Relation
  • Query Expansion
  • Short Text
  • Find Relate

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This research was undertaken as part of Project 61003130 funded by National Natural Science Foundation of China.

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© 2012 Springer-Verlag Berlin Heidelberg

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Li, L., Xiao, H., Xu, G. (2012). Finding Related Micro-blogs Based on WordNet. In: Yu, H., Yu, G., Hsu, W., Moon, YS., Unland, R., Yoo, J. (eds) Database Systems for Advanced Applications. DASFAA 2012. Lecture Notes in Computer Science, vol 7240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29023-7_13

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  • DOI: https://doi.org/10.1007/978-3-642-29023-7_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29022-0

  • Online ISBN: 978-3-642-29023-7

  • eBook Packages: Computer ScienceComputer Science (R0)